Initial Neural Net.ipynb

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   "source": [
    "# Initial Neural Net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "Now is the time to build the first NN! This is not expected to be very effective, but it should at least be better than chance.\n",
    "\n",
    "For this first pass, we use sklearn since it is very straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
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    "collapsed": true
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   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier # Basic multi-layer perceptron classifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and ready data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "data = np.load('train_data.npy')\n",
    "\n",
    "img_data = [x[0].flatten() for x in data]\n",
    "label_data = [x[1].tolist().index(1) for x in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
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   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(img_data, label_data, test_size=.2, random_state=0)\n",
    "\n",
    "# Create our NN classifier\n",
    "# See for more info: http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier\n",
    "clf = MLPClassifier(hidden_layer_sizes=(200, 100, 50), \n",
    "                    batch_size=25, learning_rate='adaptive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size=25, beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(200, 100, 50), learning_rate='adaptive',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train) # Train our model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the predictions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5050505050505051\n"
     ]
    }
   ],
   "source": [
    "predictions = clf.predict(X_test) # Returns a list of arrays of form [1,0,0] where '1' is the predicted value\n",
    "\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print('Accuracy: {}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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